Relationship of Acylcarnitines to Myocardial Ischemic Remodeling and Clinical Manifestations in Chronic Heart Failure

Belenkov, Yuri N.; Ageev, Anton A.; Kozhevnikova, Maria V.; Khabarova, Natalia V.; Krivova, Anastasia V.; Korobkova, Ekaterina O.; Popova, Ludmila V.; Emelyanov, Alexey V.; Appolonova, Svetlana A.; Moskaleva, Natalia E.; Shestakova, Ksenia M.; Privalova, Elena V.

doi:10.3390/jcdd10100438

Cited by 2 publications

References 40 publications

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Metabolomic profile for understanding heart failure classifications

Kozhevnikova,

Belenkov,

Shestakova

et al. 2024

Preprint

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Background: The existing classifications of heart failure (HF) remain a topic of debate within the cardiology community. Metabolomic profiling (MP) offers a means to define HF phenotypes based on pathophysiological changes, allowing for more precise characterization of patient groups with similar clinical profiles. MP may thus aid in refining HF classifications and offer a novel approach to phenotyping. Methods: MP was performed to 408 patients with different stages of HF. Patients with symptomatic HF were divided into phenotypes by left ventricle ejection fraction (LVEF). Liquid chromatography combined with mass-spectrometry were used for the MP. Data were analyzed using machine learning. The relationship between the incidence of all-cause death and LVEF trajectory changes and metabolomic clusters was evaluated. Follow-up period was 542 days [16;1271] in average. Results: The classification model achieved an AUC ROC - 0.91 for distinguishing of Stage A from Stage B and an AUC ROC - 0,97 for Stage B vs. Stage C using metabolomic analysis, model's performance for differentiating Stages C and D was lower (AUC ROC 0.81). For HF phenotypes, the HFrEF, HFmrEF, and HFpEF model demonstrated moderate accuracy (AUC ROC 0.74), whereas the model distinguishing HFpEF from HF with EF <50% showed good precision. The HFrEF vs. HF with EF >40% model, however, displayed low accuracy. Biostatistical processing of MP identified four metabolomic clusters, and 26 metabolites demonstrated the greatest significance (metabolites of the kynurenine and serotonin pathways of tryptophan catabolism, glutamine, riboflavin, norepinephrine, serine, long- and medium-chain acylcarnitines). Patients with reduced LVEF had the poorest prognosis (HR 1,896; 0,711?5,059), with an LVEF decrease linked to a threefold rise in all-cause mortality risk. Cluster 3 was associated with a 2,880-fold increase in all-cause mortality. Conclusions: Our findings suggest that MP provides an effective alternative approach for stratifying HF patients by stage. The observed metabolic similarities between HFpEF and HFrEF phenotypes highlight limitations in the current classification, underscoring the need to refine HF phenotyping into two primary categories. Hierarchical clustering by metabolomic profile produced a high-accuracy model, supporting MP as a valuable tool for HF classification.

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Metabolomic profile for understanding heart failure classifications

Kozhevnikova,

Belenkov,

Shestakova

et al. 2024

Preprint

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Left Ventricular Remodeling Predictors in Chronic Heart Failure of Ischemic Etiology

Ageev,

Kozhevnikova,

Tyurina

et al. 2024

Kardiologiia

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Aim To identify metabolomic and structure and function markers of remote left ventricular (LV) remodeling in patients with chronic heart failure (CHF) of ischemic etiology and LV ejection fraction (EF) <50%.Material and methods This prospective study included 56 patients with 3-4 NYHA functional class CHF of ischemic etiology (mean age, 66±7 years) and 50 patients with ischemic heart disease (IHD) without signs of CHF (69 [64; 73.7] years). Concentration of 19 amino acids, 11 products of kynurenine catabolism of tryptophan, 30 acylcarnitines with different chain lengths were measured in all participants. The metabolites that showed statistical differences between the comparison groups were then used for the analysis. Echocardiography was used to assess LV cavity remodeling at the time of the CHF patient inclusion in the study and after 6 months of follow-up. Predictors of long-term LV cavity remodeling were assessed for this cohort taking into account statistically significant echocardiographic parameters and metabolites.Results Patients with CHF of ischemic etiology, predominantly (81%) had pathological calculated types of LV remodeling (concentric and eccentric hypertrophy, 46 and 35%, respectively). However, this classification had limitations in describing this cohort. In addition, in this group, the concentrations of alanine, proline, asparagine, glycine, arginine, histidine, lysine, valine, indolyl-3-acetic acid, indolyl-3-propionic acid, C16-1-OH, and C16-OH were significantly (p<0.05) lower, and the concentrations of most medium- and long-chain acylcarnitines were higher than in patients with IHD without signs of CHF. The long-term (6 months) reverse remodeling of the LV cavity in CHF of ischemic etiology was influenced by changes in the interventricular septum thickness (hazard ratio, HR, 19.07; 95% confidence interval, CI, 1.76-206.8; p=0.006) and concentrations of anthranilic acid (HR 19.8; 95% CI 1.01-387.8; p=0.019) and asparagine (HR 8.76; 95% CI 1.07-71.4; p=0.031).Conclusion The presence of an interventricular septum thickness of more than 13.5 mm, anthranilic acid concentrations of higher than 0.235 μM/l, or an asparagine concentration of less than 135.2 μM/l in patients with CHF of ischemic etiology after 6 months of follow-up affects their achievement of LV cavity reverse remodeling.

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Relationship of Acylcarnitines to Myocardial Ischemic Remodeling and Clinical Manifestations in Chronic Heart Failure

Cited by 2 publications

References 40 publications

Metabolomic profile for understanding heart failure classifications

Metabolomic profile for understanding heart failure classifications

Left Ventricular Remodeling Predictors in Chronic Heart Failure of Ischemic Etiology

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